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Approximate Power and Sample Size Calculations with the Benjamini-Hochberg Method


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  • Ferreira José António

    (AMC - University of Amsterdam)

  • Zwinderman Aeilko H

    (University of Amsterdam)

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    We provide a method for calculating the sample size required to attain a given average power (the ratio of rejected hypotheses to the number of false hypotheses) and a given false discovery rate (the number of incorrect rejections divided by the number of rejections) in adaptive versions of the Benjamini-Hochberg method of multiple testing. The method works in an asymptotic sense as the number of hypotheses grows to infinity and under quite general conditions, and it requires data from a pilot study. The consistency of the method follows from several results in classical areas of nonparametric statistics developed in a new context of "weak" dependence.

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    Bibliographic Info

    Article provided by De Gruyter in its journal The International Journal of Biostatistics.

    Volume (Year): 2 (2006)
    Issue (Month): 1 (September)
    Pages: 1-38

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    Handle: RePEc:bpj:ijbist:v:2:y:2006:i:1:n:8

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    Cited by:
    1. Song Li & Mervyn J. Silvapulle & Param Silvapulle & Xibin Zhang, 2012. "Bayesian Approaches to Non-parametric Estimation of Densities on the Unit Interval," Monash Econometrics and Business Statistics Working Papers 3/12, Monash University, Department of Econometrics and Business Statistics.
    2. Ferreira, J.A. & Nyangoma, S.O., 2008. "A multivariate version of the Benjamini-Hochberg method," Journal of Multivariate Analysis, Elsevier, vol. 99(9), pages 2108-2124, October.
    3. Farcomeni, Alessio & Pacillo, Simona, 2011. "A conservative estimator for the proportion of false nulls based on Dvoretzky, Kiefer and Wolfowitz inequality," Statistics & Probability Letters, Elsevier, vol. 81(12), pages 1867-1870.


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